FIA NLS Models: Biomass Growth vs. Stand Age – Data Subsets
JAH
2022-12-14
Background
This analysis document compliments FIA NLS Models: Biomass Growth vs. Stand Age. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different data subsets.
Here, we fit the models using: 1) a temporally-balanced dataset, where we take the first and most-recent plot record for all plots in the dataset, 2) a temporally-balanced dataset (same as #1), but which excludes plot locations which have experienced harvest (at any point over the study interval 2000-2022)
Below the model fitting procedure is implemented by ecoprovince:
Temporally-balancing the biomass growth data set
Lets look at some quick attributes of the dataset
- The data set has 115221 observations, comprised of 58079 plots.
- The frequency of growth measurements among plots is as follows (n=1 through 5): 25558, 13784, 12967, 5656, 114.
- Thus 55.99% of plots have at least two growth measurements.
Analysis 1: Temporally-balanced analysis
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4826 1878.4
## 2 4825 1875.8 1 2.56 6.5836 0.01032 *
## 3 4790 1374.2 35 501.56 49.9492 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 20213.00
## 2 2 20208.41
## 3 3 18612.85##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.193378 0.191516 1.010 0.3127
## phi 0.004988 0.005728 0.871 0.3840
## alpha 0.646138 0.040873 15.809 <2e-16 ***
## a 0.000000 2.576629 0.000 1.0000
## b 3.455624 2.565245 1.347 0.1780
## c 31.704020 2.569992 12.336 <2e-16 ***
## d 2.849389 1.291808 2.206 0.0274 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5356 on 4790 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (37 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93396, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.7501, p-value = 9.182e-15
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 10034 3645.4
## 2 10032 3641.5 2 3.88 5.3403 0.004808 **
## 3 9747 1913.3 285 1728.23 30.8923 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 43561.12
## 2 2 43549.03
## 3 3 36432.77##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.315750 0.233404 5.637 1.78e-08 ***
## phi 0.019104 0.004506 4.240 2.26e-05 ***
## alpha 0.731615 0.028779 25.422 < 2e-16 ***
## a 0.822273 0.198416 4.144 3.44e-05 ***
## b 1.593947 0.195682 8.146 4.24e-16 ***
## c 22.040911 0.662612 33.264 < 2e-16 ***
## d 2.050107 0.202484 10.125 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4431 on 9747 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (3222 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 27 rows containing missing values (geom_point).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5452 2464.1
## 2 5451 2458.8 1 5.31 11.769 0.0006067 ***
## 3 5415 2051.7 36 407.05 29.842 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 25174.05
## 2 2 25164.28
## 3 3 24063.35##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.646969 0.155884 -4.150 3.37e-05 ***
## phi 0.016955 0.006576 2.578 0.00995 **
## alpha 0.722879 0.047031 15.370 < 2e-16 ***
## a 0.000000 3.912967 0.000 1.00000
## b 4.533953 3.910372 1.159 0.24632
## c 35.586426 3.534938 10.067 < 2e-16 ***
## d 3.089853 1.630066 1.896 0.05807 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6155 on 5415 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (40 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 2 rows containing missing values (geom_point).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2854 1139.3
## 2 2853 1139.1 1 0.22 0.5552 0.4563
## 3 2734 451.6 119 687.54 34.9781 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14497.96
## 2 2 14499.40
## 3 3 11523.33##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.13938 0.29951 -0.465 0.641717
## phi 0.03301 0.01306 2.527 0.011555 *
## alpha 0.79429 0.05883 13.502 < 2e-16 ***
## a 1.80705 0.45900 3.937 8.46e-05 ***
## b 1.62062 0.45288 3.578 0.000352 ***
## c 45.67005 4.60883 9.909 < 2e-16 ***
## d 1.86961 0.46428 4.027 5.81e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4064 on 2734 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (813 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92559, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.6967, p-value = 1.397e-14
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (geom_point).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Error in nls(fg2_2, data = G_223, start = c(ge = ge.start, a = a.start, :
## parameters without starting value in 'data': phi
## model AIC
## 1 1 25409.44
## 2 2 NA
## 3 3 22477.46##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.797118 0.151000 -5.279 1.35e-07 ***
## phi 0.000000 0.008996 0.000 1.00000
## alpha 0.603323 0.052645 11.460 < 2e-16 ***
## a 2.773083 0.618182 4.486 7.42e-06 ***
## b 1.563226 0.598919 2.610 0.00908 **
## c 28.777834 2.742419 10.494 < 2e-16 ***
## d 1.369724 0.451733 3.032 0.00244 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4961 on 5256 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1127 observations deleted due to missingness)summary
- simple model: fits
- phi model: does not fit
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 5 rows containing missing values (geom_point).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8153 4439.7
## 2 8152 4437.3 1 2.41 4.4243 0.03546 *
## 3 8030 3869.9 122 567.36 9.6496 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 42664.17
## 2 2 42661.74
## 3 3 41126.27##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.774532 0.181640 4.264 2.03e-05 ***
## phi 0.007915 0.005876 1.347 0.178
## alpha 0.886658 0.028291 31.341 < 2e-16 ***
## a 2.364704 0.286628 8.250 < 2e-16 ***
## b 3.197970 0.279054 11.460 < 2e-16 ***
## c 18.756519 0.555395 33.771 < 2e-16 ***
## d 1.467666 0.125366 11.707 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6942 on 8030 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (163 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 20 rows containing missing values (geom_point).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8129 5671.1
## 2 8128 5663.5 1 7.6 10.905 0.000963 ***
## 3 7970 4983.5 158 680.0 6.883 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 42533.94
## 2 2 42525.04
## 3 3 40898.07##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.957099 0.223185 4.288 1.82e-05 ***
## phi 0.011836 0.006192 1.911 0.056 .
## alpha 0.872885 0.028880 30.225 < 2e-16 ***
## a 3.120769 0.145123 21.504 < 2e-16 ***
## b 2.073713 0.136788 15.160 < 2e-16 ***
## c 16.362762 0.622097 26.303 < 2e-16 ***
## d 0.889109 0.070091 12.685 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7907 on 7970 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (217 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 27 rows containing missing values (geom_point).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 846 572.27
## 2 845 572.06 1 0.214 0.3167 0.5737
## 3 825 521.15 20 50.910 4.0296 9.517e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4519.781
## 2 2 4521.463
## 3 3 4375.857##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.446255 1.303516 1.110 0.267536
## phi 0.000823 0.027595 0.030 0.976214
## alpha 0.857648 0.104810 8.183 1.05e-15 ***
## a 3.267454 0.709421 4.606 4.76e-06 ***
## b 1.978059 0.689381 2.869 0.004219 **
## c 18.907883 2.683055 7.047 3.86e-12 ***
## d 0.660469 0.199429 3.312 0.000967 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7948 on 825 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (30 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits ### plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.8819, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.0363, p-value = 5.43e-05
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (geom_point).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1038 516.93
## 2 1037 516.93 1 0.00 0.00 1
## 3 975 194.87 62 322.06 25.99 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 5430.578
## 2 2 5432.578
## 3 3 4229.322##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.21188 0.64841 0.327 0.74392
## phi 0.00000 0.01848 0.000 1.00000
## alpha 0.43219 0.15080 2.866 0.00425 **
## a 0.00000 87.40235 0.000 1.00000
## b 2.97494 87.33232 0.034 0.97283
## c 14.41230 20.09081 0.717 0.47333
## d 5.62159 90.43132 0.062 0.95044
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4471 on 975 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (412 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.73795, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.0562, p-value = 0.03977
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 440 887.59
## 2 439 867.17 1 20.423 10.339 0.001399 **
## 3 415 838.84 24 28.329 0.584 0.943282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2424.892
## 2 2 2416.533
## 3 3 2319.289##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.69353 1.70607 0.407 0.68458
## phi 0.17568 0.06137 2.863 0.00441 **
## alpha 0.14853 0.42346 0.351 0.72595
## a 0.76884 0.67928 1.132 0.25835
## b 2.17686 0.96905 2.246 0.02520 *
## c 18.12283 3.84593 4.712 3.35e-06 ***
## d 1.15102 0.50348 2.286 0.02275 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 415 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (24 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9665, p-value = 3.081e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.4823, p-value = 0.0004972
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).
plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5098 1399.0
## 2 5097 1390.7 1 8.260 30.275 3.933e-08 ***
## 3 5082 1308.7 15 82.042 21.240 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19322.30
## 2 2 19294.08
## 3 3 18948.50##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.940633 0.236326 3.980 6.98e-05 ***
## phi 0.022656 0.005238 4.325 1.55e-05 ***
## alpha 0.625087 0.034889 17.917 < 2e-16 ***
## a 2.239747 0.250448 8.943 < 2e-16 ***
## b 0.747572 0.210668 3.549 0.000391 ***
## c 30.670631 2.600559 11.794 < 2e-16 ***
## d 1.163150 0.344947 3.372 0.000752 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5075 on 5082 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (15 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5256 3349.7
## 2 5255 3349.7 1 0.00 0.000 1
## 3 5227 3020.7 28 329.07 20.337 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 26437.59
## 2 2 26439.59
## 3 3 25793.14##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.256839 0.238504 1.077 0.281585
## phi 0.000000 0.008957 0.000 1.000000
## alpha 0.848303 0.072050 11.774 < 2e-16 ***
## a 2.573482 0.550708 4.673 3.04e-06 ***
## b 1.947714 0.502195 3.878 0.000106 ***
## c 28.680202 2.460324 11.657 < 2e-16 ***
## d 1.303224 0.344685 3.781 0.000158 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7602 on 5227 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (28 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 1 rows containing missing values (geom_point).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 598 363.57
## 2 597 363.56 1 0.010 0.0164 0.8982009
## 3 593 349.87 4 13.682 5.7972 0.0001398 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2558.886
## 2 2 2560.869
## 3 3 2530.660##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 3.29175 2.05903 1.599 0.110422
## phi 0.00000 0.03284 0.000 1.000000
## alpha 0.97478 0.20513 4.752 2.53e-06 ***
## a 1.48977 0.38262 3.894 0.000110 ***
## b 1.21350 0.46832 2.591 0.009801 **
## c 30.79223 2.73735 11.249 < 2e-16 ***
## d 0.40889 0.10901 3.751 0.000193 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7681 on 593 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (4 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94973, p-value = 2.048e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.9525, p-value = 0.05088
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Error in nls(fg2_3, data = G_M231, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 672 378.26
## 2 671 368.21 1 10.055 18.324 2.135e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2897.814
## 2 2 2881.574
## 3 3 NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.50558 1.89775 1.320 0.187188
## phi 0.10690 0.03246 3.293 0.001043 **
## a 1.59568 0.41897 3.809 0.000153 ***
## b 2.61731 1.09677 2.386 0.017292 *
## c 8.32447 0.36083 23.070 < 2e-16 ***
## d 0.14000 0.04573 3.062 0.002289 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7408 on 671 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.95405, p-value = 1.085e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.0879, p-value = 3.621e-07
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M242 - Cascade Mixed Forest
model selection 1
## Error in nls(fg2_1, data = G_M242, start = c(ge = ge.start, a = a.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(fg2_2, data = G_M242, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(fg2_3, data = G_M242, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M242.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 164 4.2484
## 2 163 4.1772 1 0.071264 2.7809 0.09732 .
## 3 162 4.0932 1 0.083971 3.3234 0.07014 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 651.0310
## 2 2 650.1721
## 3 3 648.7402##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -2.40380 1.18751 -2.024 0.044589 *
## phi 0.11429 0.06088 1.877 0.062290 .
## alpha 0.63487 0.29797 2.131 0.034624 *
## a 2.65500 1.78504 1.487 0.138863
## b 6.74457 4.26227 1.582 0.115511
## c 43.36414 5.98332 7.248 1.64e-11 ***
## d 0.69853 0.17766 3.932 0.000125 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.159 on 162 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (171 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93958, p-value = 1.415e-06
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 1.6143, p-value = 0.1065
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 218 35.122
## 2 217 35.122 1 0.000 0.000 0.9994
## 3 211 21.490 6 13.632 22.308 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 891.5661
## 2 2 893.5661
## 3 3 774.9166##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.51871 1.78829 -0.290 0.772055
## phi 0.00000 0.04936 0.000 1.000000
## alpha 0.82065 0.20888 3.929 0.000116 ***
## a 0.00000 4.66310 0.000 1.000000
## b 1.92469 4.69391 0.410 0.682193
## c 59.02216 9.54875 6.181 3.24e-09 ***
## d 1.38505 2.26567 0.611 0.541645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3191 on 211 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (92 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.80029, p-value = 5.396e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.89306, p-value = 0.3718
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 3 |
| 212 | Laurentian Mixed Forest | 3 |
| 221 | Eastern Broadleaf Forest | 3 |
| 222 | Midwest Broadleaf Forest | 3 |
| 223 | Central Interior Broadleaf Forest | 3 |
| 231 | Southeastern Mixed Forest | 3 |
| 232 | Outer Coastal Plain Mixed Forest | 3 |
| 234 | Lower Mississippi Riverine Forest | 3 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 3 |
| 255 | Prairie Parkland (Subtropical) | 3 |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | NA |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 3 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 3 |
| M223 | Ozark Broadleaf Forest Meadow | 3 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 3 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 3 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | ge | ge.variance | ge.2.5 | ge.97.5 | phi | phi.variance | phi.2.5 | phi.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | a | a.2.5 | a.97.5 | b | b.2.5 | b.97.5 | c | c.2.5 | c.97.5 | d | d.2.5 | d.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4834 | 2417 | 0.1933784 | 0.0366785 | -0.1820813 | 0.5688382 | 0.0049875 | 0.0000328 | -0.0062429 | 0.0162180 | 0.6461380 | 0.0016706 | 0.5660087 | 0.7262673 | 0.0000000 | -5.0513757 | 5.051376 | 3.4556239 | -1.5734339 | 8.484682 | 31.704020 | 26.665654 | 36.742385 | 2.8493889 | 0.3168514 | 5.3819264 |
| 212 | Laurentian Mixed Forest | east | 12976 | 6488 | 1.3157502 | 0.0544772 | 0.8582309 | 1.7732694 | 0.0191040 | 0.0000203 | 0.0102712 | 0.0279367 | 0.7316154 | 0.0008282 | 0.6752022 | 0.7880285 | 0.8222733 | 0.4333372 | 1.211210 | 1.5939474 | 1.2103701 | 1.977525 | 22.040911 | 20.742054 | 23.339768 | 2.0501066 | 1.6531954 | 2.4470177 |
| 221 | Eastern Broadleaf Forest | east | 5462 | 2731 | -0.6469689 | 0.0242999 | -0.9525644 | -0.3413734 | 0.0169548 | 0.0000432 | 0.0040638 | 0.0298457 | 0.7228794 | 0.0022119 | 0.6306803 | 0.8150785 | 0.0000000 | -7.6709889 | 7.670989 | 4.5339529 | -3.1319484 | 12.199854 | 35.586426 | 28.656526 | 42.516325 | 3.0898533 | -0.1057315 | 6.2854381 |
| 222 | Midwest Broadleaf Forest | east | 3554 | 1777 | -0.1393791 | 0.0897082 | -0.7266744 | 0.4479162 | 0.0330114 | 0.0001706 | 0.0073976 | 0.0586251 | 0.7942885 | 0.0034605 | 0.6789413 | 0.9096358 | 1.8070512 | 0.9070230 | 2.707079 | 1.6206198 | 0.7326025 | 2.508637 | 45.670049 | 36.632901 | 54.707197 | 1.8696060 | 0.9592353 | 2.7799766 |
| 223 | Central Interior Broadleaf Forest | east | 6390 | 3195 | -0.7971176 | 0.0228011 | -1.0931413 | -0.5010940 | 0.0000000 | 0.0000809 | -0.0176361 | 0.0176361 | 0.6033233 | 0.0027715 | 0.5001176 | 0.7065291 | 2.7730826 | 1.5611891 | 3.984976 | 1.5632260 | 0.3890954 | 2.737357 | 28.777834 | 23.401553 | 34.154116 | 1.3697238 | 0.4841395 | 2.2553080 |
| 231 | Southeastern Mixed Forest | east | 8200 | 4100 | 0.7745322 | 0.0329929 | 0.4184716 | 1.1305928 | 0.0079151 | 0.0000345 | -0.0036026 | 0.0194328 | 0.8866583 | 0.0008004 | 0.8312008 | 0.9421157 | 2.3647040 | 1.8028389 | 2.926569 | 3.1979701 | 2.6509527 | 3.744987 | 18.756519 | 17.667800 | 19.845237 | 1.4676664 | 1.2219158 | 1.7134170 |
| 232 | Outer Coastal Plain Mixed Forest | east | 8194 | 4097 | 0.9570986 | 0.0498115 | 0.5195980 | 1.3945993 | 0.0118356 | 0.0000383 | -0.0003022 | 0.0239733 | 0.8728854 | 0.0008340 | 0.8162737 | 0.9294972 | 3.1207692 | 2.8362895 | 3.405249 | 2.0737128 | 1.8055724 | 2.341853 | 16.362762 | 15.143289 | 17.582235 | 0.8891088 | 0.7517122 | 1.0265053 |
| 234 | Lower Mississippi Riverine Forest | east | 862 | 431 | 1.4462552 | 1.6991535 | -1.1123425 | 4.0048529 | 0.0008230 | 0.0007615 | -0.0533425 | 0.0549885 | 0.8576484 | 0.0109851 | 0.6519227 | 1.0633741 | 3.2674540 | 1.8749715 | 4.659936 | 1.9780593 | 0.6249119 | 3.331207 | 18.907883 | 13.641466 | 24.174300 | 0.6604686 | 0.2690210 | 1.0519163 |
| 242 | Pacific Lowland Mixed Forest | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1394 | 697 | 0.2118765 | 0.4204376 | -1.0605665 | 1.4843194 | 0.0000000 | 0.0003415 | -0.0362643 | 0.0362643 | 0.4321891 | 0.0227421 | 0.1362495 | 0.7281286 | 0.0000000 | -171.5183718 | 171.518372 | 2.9749371 | -168.4060055 | 174.355880 | 14.412295 | -25.013915 | 53.838505 | 5.6215915 | -171.8408436 | 183.0840266 |
| 255 | Prairie Parkland (Subtropical) | east | 446 | 223 | 0.6935298 | 2.9106676 | -2.6600823 | 4.0471419 | 0.1756790 | 0.0037663 | 0.0550435 | 0.2963144 | 0.1485288 | 0.1793183 | -0.6838649 | 0.9809225 | 0.7688379 | -0.5664169 | 2.104093 | 2.1768590 | 0.2720068 | 4.081711 | 18.122829 | 10.562891 | 25.682767 | 1.1510239 | 0.1613315 | 2.1407163 |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 118 | 59 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 154 | 77 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 341 | Intermountain Semi-Desert and Desert | interior west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5104 | 2552 | 0.9406330 | 0.0558499 | 0.4773325 | 1.4039335 | 0.0226561 | 0.0000274 | 0.0123873 | 0.0329249 | 0.6250871 | 0.0012172 | 0.5566902 | 0.6934840 | 2.2397473 | 1.7487612 | 2.730733 | 0.7475724 | 0.3345724 | 1.160572 | 30.670631 | 25.572414 | 35.768848 | 1.1631498 | 0.4869044 | 1.8393951 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5262 | 2631 | 0.2568394 | 0.0568842 | -0.2107285 | 0.7244073 | 0.0000000 | 0.0000802 | -0.0175596 | 0.0175596 | 0.8483035 | 0.0051911 | 0.7070563 | 0.9895506 | 2.5734825 | 1.4938646 | 3.653100 | 1.9477135 | 0.9632021 | 2.932225 | 28.680202 | 23.856938 | 33.503466 | 1.3032238 | 0.6274974 | 1.9789501 |
| M223 | Ozark Broadleaf Forest Meadow | east | 604 | 302 | 3.2917489 | 4.2395904 | -0.7521226 | 7.3356205 | 0.0000000 | 0.0010788 | -0.0645063 | 0.0645063 | 0.9747756 | 0.0420796 | 0.5719000 | 1.3776512 | 1.4897731 | 0.7383163 | 2.241230 | 1.2135029 | 0.2937271 | 2.133279 | 30.792233 | 25.416156 | 36.168310 | 0.4088893 | 0.1947995 | 0.6229792 |
| M231 | Ouachita Mixed Forest | east | 678 | 339 | 2.5055787 | 3.6014372 | -1.2206550 | 6.2318123 | 0.1069028 | 0.0010539 | 0.0431601 | 0.1706455 | NA | NA | NA | NA | 1.5956826 | 0.7730305 | 2.418335 | 2.6173145 | 0.4637910 | 4.770838 | 8.324472 | 7.615972 | 9.032971 | 0.1400017 | 0.0502173 | 0.2297860 |
| M242 | Cascade Mixed Forest | pacific | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 340 | 170 | -2.4038014 | 1.4101854 | -4.7488006 | -0.0588022 | 0.1142913 | 0.0037069 | -0.0059378 | 0.2345204 | 0.6348730 | 0.0887841 | 0.0464738 | 1.2232722 | 2.6550045 | -0.8699451 | 6.179954 | 6.7445655 | -1.6722040 | 15.161335 | 43.364143 | 31.548785 | 55.179500 | 0.6985345 | 0.3477068 | 1.0493621 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | interior west | 310 | 155 | -0.5187127 | 3.1979869 | -4.0439195 | 3.0064940 | 0.0000000 | 0.0024368 | -0.0973102 | 0.0973102 | 0.8206473 | 0.0436301 | 0.4088918 | 1.2324027 | 0.0000000 | -9.1922342 | 9.192234 | 1.9246863 | -7.3282724 | 11.177645 | 59.022159 | 40.198997 | 77.845321 | 1.3850538 | -3.0812017 | 5.8513094 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot ge
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning: package 'ggnewscale' was built under R version 4.2.1## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot phi (effect of DeltaPDSI)
plot alpha (biomass growth compensation effect)
plot a coefficient
## Warning: Removed 20 rows containing missing values (geom_point).
plot b coefficient
## Warning: Removed 20 rows containing missing values (geom_point).
plot c coefficient
## Warning: Removed 1 rows containing missing values (geom_hline).## Warning: Removed 21 rows containing missing values (geom_point).
plot d coefficient
## Warning: Removed 21 rows containing missing values (geom_point).
Caclulations - weighted averages
ge (stand biomass growth enhancement factor in % 2000-2021)
## region weighted.ge weighted.ge.std_Error 95 % CI, upper
## 1 entire US 0.519535745 0.080980585 0.6782576920
## 2 pacific -0.012565611 0.006207592 -0.0003987315
## 3 east 0.534573619 0.080291188 0.6919443480
## 4 interior west -0.002472263 0.008523268 0.0142333421
## 95 % CI, lower
## 1 0.36081380
## 2 -0.02473249
## 3 0.37720289
## 4 -0.01917787phi (effect of DeltaPDSI)
## region weighted.phi weighted.phi.std_Error 95 % CI, upper
## 1 entire US 0.0146037098 0.0022952268 0.0191023544
## 2 pacific 0.0005974453 0.0003182659 0.0012212465
## 3 east 0.0140062644 0.0022608445 0.0184375196
## 4 interior west 0.0000000000 0.0002352772 0.0004611432
## 95 % CI, lower
## 1 1.010507e-02
## 2 -2.635576e-05
## 3 9.575009e-03
## 4 -4.611432e-04alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.734719810 0.0137897212 0.761747663
## 2 pacific 0.003318730 0.0015575878 0.006371602
## 3 east 0.727489751 0.0136652560 0.754273653
## 4 interior west 0.003911329 0.0009955451 0.005862597
## 95 % CI, lower
## 1 0.7076919565
## 2 0.0002658578
## 3 0.7007058496
## 4 0.0019600606Analysis 2: Temporally-balanced, No-harvest
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3752 1425.7
## 2 3751 1424.8 1 0.85 2.2493 0.1338
## 3 3719 1102.3 32 322.50 34.0013 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 15719.00
## 2 2 15718.75
## 3 3 14669.80##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.225077 0.224240 1.004 0.316
## phi 0.003788 0.006570 0.577 0.564
## alpha 0.710688 0.088408 8.039 1.21e-15 ***
## a 0.000000 3.906624 0.000 1.000
## b 3.416812 3.887564 0.879 0.380
## c 30.345394 2.966165 10.231 < 2e-16 ***
## d 2.920245 1.991604 1.466 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5444 on 3719 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (34 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92666, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.0029, p-value = 2.507e-12
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (geom_point).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8121 2830.5
## 2 8119 2827.5 2 3.0 4.3049 0.01353 *
## 3 7863 1594.0 256 1233.5 23.7684 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 34848.85
## 2 2 34838.85
## 3 3 29572.19##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.073352 0.243939 4.400 1.10e-05 ***
## phi 0.016990 0.005101 3.331 0.000871 ***
## alpha 0.576091 0.049898 11.545 < 2e-16 ***
## a 0.936434 0.201091 4.657 3.26e-06 ***
## b 1.504802 0.194661 7.730 1.20e-14 ***
## c 22.200089 0.815840 27.211 < 2e-16 ***
## d 1.905112 0.207828 9.167 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4502 on 7863 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (2612 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 21 rows containing missing values (geom_point).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4467 1979.1
## 2 4466 1976.0 1 3.114 7.037 0.008012 **
## 3 4433 1730.7 33 245.343 19.044 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 20675.28
## 2 2 20670.24
## 3 3 19973.85##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.646589 0.175698 -3.680 0.000236 ***
## phi 0.016471 0.007435 2.215 0.026789 *
## alpha 0.688822 0.085486 8.058 9.91e-16 ***
## a 0.000000 3.641690 0.000 1.000000
## b 4.525004 3.638859 1.244 0.213741
## c 35.492113 3.519003 10.086 < 2e-16 ***
## d 2.935701 1.455958 2.016 0.043825 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6248 on 4433 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (36 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.88597, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -11.381, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (geom_point).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2250 930.63
## 2 2249 930.48 1 0.15 0.3619 0.5475
## 3 2136 363.61 113 566.87 29.4691 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 11488.094
## 2 2 11489.731
## 3 3 9056.514##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.25657 0.32601 -0.787 0.431373
## phi 0.02804 0.01469 1.909 0.056342 .
## alpha 0.76351 0.08885 8.594 < 2e-16 ***
## a 2.04022 0.41787 4.882 1.13e-06 ***
## b 1.39677 0.40873 3.417 0.000644 ***
## c 48.40263 5.47352 8.843 < 2e-16 ***
## d 1.62332 0.45453 3.571 0.000363 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4126 on 2136 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (655 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93933, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.8368, p-value = 4.621e-15
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 5 rows containing missing values (geom_point).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Error in nls(fg2_2, data = G_223, start = c(ge = ge.start, a = a.start, :
## parameters without starting value in 'data': phi
## model AIC
## 1 1 20999.77
## 2 2 NA
## 3 3 18427.20##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.85404 0.16575 -5.153 2.69e-07 ***
## phi 0.00000 0.01008 0.000 1.00000
## alpha 0.50058 0.09405 5.322 1.08e-07 ***
## a 2.84500 0.51862 5.486 4.36e-08 ***
## b 1.48126 0.50082 2.958 0.00312 **
## c 30.63534 2.85138 10.744 < 2e-16 ***
## d 1.23205 0.37968 3.245 0.00118 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4994 on 4267 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (848 observations deleted due to missingness)summary
- simple model: fits
- phi model: does not fit
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 4 rows containing missing values (geom_point).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6402 3141.1
## 2 6401 3140.6 1 0.491 1.0004 0.3173
## 3 6291 2932.9 110 207.626 4.0486 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 33390.23
## 2 2 33391.23
## 3 3 32584.79##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.679888 0.200724 3.387 0.00071 ***
## phi 0.008068 0.006643 1.214 0.22463
## alpha 0.766437 0.072229 10.611 < 2e-16 ***
## a 1.844170 0.393290 4.689 2.8e-06 ***
## b 3.612811 0.391088 9.238 < 2e-16 ***
## c 19.509386 0.623461 31.292 < 2e-16 ***
## d 1.668083 0.161068 10.356 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6828 on 6291 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (146 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 15 rows containing missing values (geom_point).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6478 4130.0
## 2 6477 4127.9 1 2.104 3.3010 0.06929 .
## 3 6349 3890.9 128 236.974 3.0209 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 33938.10
## 2 2 33936.79
## 3 3 33074.28##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.048845 0.265513 3.950 7.89e-05 ***
## phi 0.006725 0.007023 0.958 0.338
## alpha 0.721818 0.061190 11.796 < 2e-16 ***
## a 3.004204 0.164092 18.308 < 2e-16 ***
## b 1.947009 0.150801 12.911 < 2e-16 ***
## c 16.907229 0.753991 22.424 < 2e-16 ***
## d 0.908464 0.085197 10.663 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7828 on 6349 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (184 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
## Warning: Removed 31 rows containing missing values (geom_point).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 701 435.21
## 2 700 435.13 1 0.081 0.1303 0.7182252
## 3 680 406.44 20 28.681 2.3993 0.0005911 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3724.973
## 2 2 3726.842
## 3 3 3614.833##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.10947 1.25666 0.883 0.37761
## phi 0.00000 0.02986 0.000 1.00000
## alpha 0.81627 0.17317 4.714 2.95e-06 ***
## a 3.24768 0.74265 4.373 1.42e-05 ***
## b 1.91997 0.65980 2.910 0.00373 **
## c 21.38038 3.64236 5.870 6.81e-09 ***
## d 0.70116 0.25066 2.797 0.00530 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7731 on 680 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (27 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits ### plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.902, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.9476, p-value = 0.003203
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 924 488.51
## 2 923 488.51 1 0.00 0.000 1
## 3 864 182.65 59 305.87 24.523 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4877.980
## 2 2 4879.980
## 3 3 3786.144##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.29503 0.73064 0.404 0.6865
## phi 0.00000 0.01979 0.000 1.0000
## alpha 0.42949 0.19039 2.256 0.0243 *
## a 0.00000 253.81733 0.000 1.0000
## b 2.84800 253.71862 0.011 0.9910
## c 11.23783 33.74535 0.333 0.7392
## d 7.80526 367.62748 0.021 0.9831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4598 on 864 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (349 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.72826, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.3062, p-value = 0.0009458
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (geom_point).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 408 1147.6
## 2 407 1123.7 1 23.880 8.6491 0.003459 **
## 3 383 1084.0 24 39.738 0.5850 0.942436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2378.960
## 2 2 2372.275
## 3 3 2273.031##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 3.58436 4.80697 0.746 0.4563
## phi 0.19284 0.08137 2.370 0.0183 *
## alpha 0.21593 0.61601 0.351 0.7261
## a 0.00000 1.11339 0.000 1.0000
## b 1.94993 1.53323 1.272 0.2042
## c 14.89018 2.27209 6.554 1.82e-10 ***
## d 1.57113 0.89316 1.759 0.0794 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.682 on 383 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (24 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96949, p-value = 2.811e-07
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.7178, p-value = 0.000201
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (geom_point).
plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3960 965.64
## 2 3959 962.49 1 3.1497 12.9556 0.0003229 ***
## 3 3945 946.55 14 15.9474 4.7475 1.009e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 14718.66
## 2 2 14707.70
## 3 3 14609.22##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.186426 0.286790 4.137 3.59e-05 ***
## phi 0.021283 0.005786 3.678 0.000238 ***
## alpha 0.460344 0.086347 5.331 1.03e-07 ***
## a 2.184626 0.213334 10.240 < 2e-16 ***
## b 0.646730 0.161405 4.007 6.27e-05 ***
## c 29.329397 2.872077 10.212 < 2e-16 ***
## d 1.089173 0.329610 3.304 0.000960 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4898 on 3945 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (14 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.98312, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -7.5409, p-value = 4.666e-14
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4682 2980.7
## 2 4681 2980.7 1 0.0 0.000 1
## 3 4654 2695.9 27 284.8 18.209 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 23616.45
## 2 2 23618.45
## 3 3 23048.87##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.243551 0.251964 0.967 0.333789
## phi 0.000000 0.009489 0.000 1.000000
## alpha 0.897981 0.118962 7.548 5.27e-14 ***
## a 2.612340 0.554196 4.714 2.50e-06 ***
## b 1.898612 0.498966 3.805 0.000144 ***
## c 28.445897 2.601549 10.934 < 2e-16 ***
## d 1.279176 0.356674 3.586 0.000339 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7611 on 4654 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (27 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.86793, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.5443, p-value = 5.976e-11
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 524 311.92
## 2 523 311.92 1 0.0002 0.0003 0.98587
## 3 519 306.31 4 5.6074 2.3752 0.05114 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2253.534
## 2 2 2255.533
## 3 3 2239.595##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.7321 1.9279 1.417 0.157051
## phi 0.0000 0.0354 0.000 1.000000
## alpha 0.7405 0.3428 2.160 0.031217 *
## a 1.5518 0.4098 3.786 0.000171 ***
## b 1.1397 0.4550 2.505 0.012550 *
## c 31.3444 3.2501 9.644 < 2e-16 ***
## d 0.4420 0.1369 3.229 0.001321 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7682 on 519 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (4 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94481, p-value = 4.385e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.8213, p-value = 0.06856
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Error in nls(fg2_1, data = G_M231, start = c(ge = ge.start, a = a.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(fg2_2, data = G_M231, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(fg2_3, data = G_M231, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 672 378.26
## 2 671 368.21 1 10.055 18.324 2.135e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2897.814
## 2 2 2881.574
## 3 3 NA##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.50558 1.89775 1.320 0.187188
## phi 0.10690 0.03246 3.293 0.001043 **
## a 1.59568 0.41897 3.809 0.000153 ***
## b 2.61731 1.09677 2.386 0.017292 *
## c 8.32447 0.36083 23.070 < 2e-16 ***
## d 0.14000 0.04573 3.062 0.002289 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7408 on 671 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.95405, p-value = 1.085e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.0879, p-value = 3.621e-07
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M242 - Cascade Mixed Forest
model selection 1
## Error in nls(fg2_1, data = G_M242, start = c(ge = ge.start, a = a.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(fg2_2, data = G_M242, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(fg2_3, data = G_M242, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M242.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 155 3.8918
## 2 154 3.7881 1 0.103657 4.2140 0.04178 *
## 3 153 3.7187 1 0.069373 2.8542 0.09317 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 609.8531
## 2 2 607.5337
## 3 3 606.5764##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -2.37395 1.26971 -1.870 0.063439 .
## phi 0.14571 0.05622 2.592 0.010476 *
## alpha 0.63651 0.32009 1.989 0.048537 *
## a 2.98472 2.02026 1.477 0.141625
## b 6.78463 4.47605 1.516 0.131642
## c 41.94215 6.16319 6.805 2.15e-10 ***
## d 0.66258 0.17696 3.744 0.000256 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1559 on 153 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (162 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94206, p-value = 3.972e-06
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.61669, p-value = 0.5374
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 3: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 181 52.155
## 2 180 52.155 1 0.000 0.0000 1
## 3 174 39.478 6 12.677 9.3126 7.463e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 809.9020
## 2 2 811.9020
## 3 3 746.8285##
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * ge/100) * (1 +
## phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -2.84468 0.40013 -7.109 2.89e-11 ***
## phi 0.06435 0.04798 1.341 0.18157
## alpha 1.07237 0.35853 2.991 0.00318 **
## a 0.00000 15.87378 0.000 1.00000
## b 4.06202 15.63642 0.260 0.79534
## c 67.09623 16.12712 4.160 4.98e-05 ***
## d 1.37153 3.64708 0.376 0.70733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4763 on 174 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (77 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.83503, p-value = 4.863e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.96554, p-value = 0.3343
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 3 rows containing missing values (geom_point).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 3 |
| 212 | Laurentian Mixed Forest | 3 |
| 221 | Eastern Broadleaf Forest | 3 |
| 222 | Midwest Broadleaf Forest | 3 |
| 223 | Central Interior Broadleaf Forest | 3 |
| 231 | Southeastern Mixed Forest | 3 |
| 232 | Outer Coastal Plain Mixed Forest | 3 |
| 234 | Lower Mississippi Riverine Forest | 3 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 3 |
| 255 | Prairie Parkland (Subtropical) | 3 |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | NA |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 3 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 3 |
| M223 | Ozark Broadleaf Forest Meadow | 3 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 3 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | NA |
| M334 | Black Hills Coniferous Forest | 3 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | ge | ge.variance | ge.2.5 | ge.97.5 | phi | phi.variance | phi.2.5 | phi.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | a | a.2.5 | a.97.5 | b | b.2.5 | b.97.5 | c | c.2.5 | c.97.5 | d | d.2.5 | d.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 4834 | 2417 | 0.2250766 | 0.0502837 | -0.2145693 | 0.6647226 | 0.0037875 | 0.0000432 | -0.0090926 | 0.0166677 | 0.7106877 | 0.0078160 | 0.5373549 | 0.8840206 | 0.0000000 | -7.6593360 | 7.659336 | 3.4168122 | -4.2051547 | 11.0387791 | 30.345394 | 24.529925 | 36.160864 | 2.9202451 | -0.9844974 | 6.824988 |
| 212 | Laurentian Mixed Forest | east | 12976 | 6488 | 1.0733519 | 0.0595063 | 0.5951663 | 1.5515375 | 0.0169898 | 0.0000260 | 0.0069903 | 0.0269893 | 0.5760909 | 0.0024898 | 0.4782773 | 0.6739044 | 0.9364341 | 0.5422423 | 1.330626 | 1.5048016 | 1.1232152 | 1.8863879 | 22.200089 | 20.600826 | 23.799351 | 1.9051119 | 1.4977135 | 2.312510 |
| 221 | Eastern Broadleaf Forest | east | 5462 | 2731 | -0.6465893 | 0.0308696 | -0.9910443 | -0.3021343 | 0.0164707 | 0.0000553 | 0.0018946 | 0.0310468 | 0.6888216 | 0.0073079 | 0.5212256 | 0.8564177 | 0.0000000 | -7.1395298 | 7.139530 | 4.5250041 | -2.6089765 | 11.6589846 | 35.492113 | 28.593110 | 42.391115 | 2.9357011 | 0.0812975 | 5.790105 |
| 222 | Midwest Broadleaf Forest | east | 3554 | 1777 | -0.2565694 | 0.1062837 | -0.8959032 | 0.3827644 | 0.0280413 | 0.0002157 | -0.0007587 | 0.0568413 | 0.7635138 | 0.0078938 | 0.5892780 | 0.9377496 | 2.0402172 | 1.2207385 | 2.859696 | 1.3967743 | 0.5952238 | 2.1983247 | 48.402626 | 37.668636 | 59.136616 | 1.6233158 | 0.7319490 | 2.514683 |
| 223 | Central Interior Broadleaf Forest | east | 6390 | 3195 | -0.8540404 | 0.0274733 | -1.1789980 | -0.5290829 | 0.0000000 | 0.0001016 | -0.0197641 | 0.0197641 | 0.5005751 | 0.0088463 | 0.3161791 | 0.6849710 | 2.8450004 | 1.8282307 | 3.861770 | 1.4812635 | 0.4993934 | 2.4631337 | 30.635340 | 25.045148 | 36.225532 | 1.2320518 | 0.4876882 | 1.976415 |
| 231 | Southeastern Mixed Forest | east | 8200 | 4100 | 0.6798877 | 0.0402901 | 0.2864004 | 1.0733749 | 0.0080677 | 0.0000441 | -0.0049553 | 0.0210908 | 0.7664374 | 0.0052170 | 0.6248448 | 0.9080300 | 1.8441701 | 1.0731878 | 2.615152 | 3.6128114 | 2.8461464 | 4.3794765 | 19.509386 | 18.287190 | 20.731581 | 1.6680829 | 1.3523338 | 1.983832 |
| 232 | Outer Coastal Plain Mixed Forest | east | 8194 | 4097 | 1.0488449 | 0.0704973 | 0.5283491 | 1.5693407 | 0.0067246 | 0.0000493 | -0.0070429 | 0.0204921 | 0.7218179 | 0.0037443 | 0.6018640 | 0.8417717 | 3.0042044 | 2.6825286 | 3.325880 | 1.9470091 | 1.6513878 | 2.2426303 | 16.907229 | 15.429153 | 18.385306 | 0.9084636 | 0.7414489 | 1.075478 |
| 234 | Lower Mississippi Riverine Forest | east | 862 | 431 | 1.1094732 | 1.5791848 | -1.3579194 | 3.5768658 | 0.0000000 | 0.0008916 | -0.0586276 | 0.0586276 | 0.8162650 | 0.0299873 | 0.4762559 | 1.1562741 | 3.2476766 | 1.7895158 | 4.705837 | 1.9199703 | 0.6244708 | 3.2154699 | 21.380379 | 14.228763 | 28.531995 | 0.7011640 | 0.2090084 | 1.193320 |
| 242 | Pacific Lowland Mixed Forest | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1394 | 697 | 0.2950328 | 0.5338401 | -1.1390113 | 1.7290769 | 0.0000000 | 0.0003918 | -0.0388516 | 0.0388516 | 0.4294912 | 0.0362479 | 0.0558125 | 0.8031700 | 0.0000000 | -498.1706832 | 498.170683 | 2.8480001 | -495.1289443 | 500.8249444 | 11.237830 | -54.994630 | 77.470290 | 7.8052604 | -713.7421392 | 729.352660 |
| 255 | Prairie Parkland (Subtropical) | east | 446 | 223 | 3.5843628 | 23.1069157 | -5.8669828 | 13.0357084 | 0.1928428 | 0.0066213 | 0.0328523 | 0.3528334 | 0.2159275 | 0.3794669 | -0.9952550 | 1.4271100 | 0.0000000 | -2.1891289 | 2.189129 | 1.9499328 | -1.0646737 | 4.9645393 | 14.890176 | 10.422840 | 19.357511 | 1.5711296 | -0.1849767 | 3.327236 |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 118 | 59 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 154 | 77 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 341 | Intermountain Semi-Desert and Desert | interior west | 4 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 2 | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 66 | 33 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 5104 | 2552 | 1.1864259 | 0.0822486 | 0.6241549 | 1.7486969 | 0.0212827 | 0.0000335 | 0.0099391 | 0.0326263 | 0.4603438 | 0.0074558 | 0.2910551 | 0.6296325 | 2.1846261 | 1.7663702 | 2.602882 | 0.6467296 | 0.3302847 | 0.9631744 | 29.329397 | 23.698503 | 34.960291 | 1.0891726 | 0.4429511 | 1.735394 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 5262 | 2631 | 0.2435508 | 0.0634856 | -0.2504171 | 0.7375188 | 0.0000000 | 0.0000900 | -0.0186038 | 0.0186038 | 0.8979809 | 0.0141519 | 0.6647595 | 1.1312023 | 2.6123404 | 1.5258535 | 3.698827 | 1.8986116 | 0.9204012 | 2.8768220 | 28.445897 | 23.345627 | 33.546167 | 1.2791762 | 0.5799253 | 1.978427 |
| M223 | Ozark Broadleaf Forest Meadow | east | 604 | 302 | 2.7320551 | 3.7168247 | -1.0554053 | 6.5195154 | 0.0000000 | 0.0012530 | -0.0695410 | 0.0695410 | 0.7404898 | 0.1175070 | 0.0670574 | 1.4139222 | 1.5517631 | 0.7466424 | 2.356884 | 1.1397087 | 0.2459018 | 2.0335157 | 31.344434 | 24.959377 | 37.729491 | 0.4419843 | 0.1730677 | 0.710901 |
| M231 | Ouachita Mixed Forest | east | 678 | 339 | 2.5055787 | 3.6014372 | -1.2206550 | 6.2318123 | 0.1069028 | NA | 0.0431601 | 0.1706455 | NA | NA | NA | NA | 1.5956826 | 0.7730305 | 2.418335 | 2.6173145 | 0.4637910 | 4.7708381 | 8.324472 | 7.615972 | 9.032971 | 0.1400017 | 0.0502173 | 0.229786 |
| M242 | Cascade Mixed Forest | pacific | 34 | 17 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 340 | 170 | -2.3739529 | 1.6121586 | -4.8823758 | 0.1344701 | 0.1457104 | 0.0031610 | 0.0346370 | 0.2567838 | 0.6365113 | 0.1024587 | 0.0041412 | 1.2688815 | 2.9847243 | -1.0064864 | 6.975935 | 6.7846349 | -2.0582015 | 15.6274712 | 41.942146 | 29.766217 | 54.118076 | 0.6625831 | 0.3129854 | 1.012181 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 8 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 20 | 10 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 22 | 11 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M334 | Black Hills Coniferous Forest | interior west | 310 | 155 | -2.8446825 | 0.1601056 | -3.6344194 | -2.0549455 | 0.0643523 | 0.0023018 | -0.0303401 | 0.1590448 | 1.0723691 | 0.1285439 | 0.3647410 | 1.7799972 | 0.0000000 | -31.3299531 | 31.329953 | 4.0620236 | -26.7994526 | 34.9234997 | 67.096231 | 35.266266 | 98.926195 | 1.3715257 | -5.8266897 | 8.569741 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot ge
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot phi (effect of DeltaPDSI)
plot alpha (biomass growth compensation effect)
plot a coefficient
## Warning: Removed 20 rows containing missing values (geom_point).
plot b coefficient
## Warning: Removed 20 rows containing missing values (geom_point).
plot c coefficient
## Warning: Removed 1 rows containing missing values (geom_hline).## Warning: Removed 21 rows containing missing values (geom_point).
plot d coefficient
## Warning: Removed 21 rows containing missing values (geom_point).
Caclulations - weighted averages
ge (stand biomass growth enhancement factor in % 2000-2021)
## region weighted.ge weighted.ge.std_Error 95 % CI, upper 95 % CI, lower
## 1 entire US 0.48042203 0.092174912 0.661084855 0.29975920
## 2 pacific -0.01240958 0.006637261 0.000599451 -0.02541861
## 3 east 0.50638980 0.091915854 0.686544869 0.32623472
## 4 interior west -0.01355819 0.001907089 -0.009820292 -0.01729608phi (effect of DeltaPDSI)
## region weighted.phi weighted.phi.std_Error 95 % CI, upper
## 1 entire US 0.0136258497 0.0025422716 0.0186087021
## 2 pacific 0.0007616851 0.0002938991 0.0013377272
## 3 east 0.0125574518 0.0025148518 0.0174865614
## 4 interior west 0.0003067129 0.0002286672 0.0007549007
## 95 % CI, lower
## 1 0.0086429973
## 2 0.0001856429
## 3 0.0076283422
## 4 -0.0001414749alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.649645428 0.025283449 0.699200988
## 2 pacific 0.003327294 0.001673245 0.006606854
## 3 east 0.641207061 0.025170082 0.690540421
## 4 interior west 0.005111073 0.001708809 0.008460339
## 95 % CI, lower
## 1 6.000899e-01
## 2 4.773384e-05
## 3 5.918737e-01
## 4 1.761807e-03